Artificial neural network (ANN) modeling for simultaneous removal of a binary mixture of Pb(II) and Cu(II) by cobalt hydroxide nano-flakes

Abstract

A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Cu(II) and Pb(II) ion removal from aqueous solution by cobalt hydroxide nano-flakes. It is based on experimental sets obtained from a D-optimal design. The input variables to the neural network were as follows: the initial concentration of Pb(II) and Cu (II) ions (mg L⁻¹), initial pH, and sorbent mass (g). The configuration of the backpropagation neural network for both Cu(II) and Pb (II) ions was a tangent sigmoid transfer function (tansig) at the hidden layer, linear transfer function (purelin) at the output layer, and Levenberg–Marquardt training algorithm (LMA). ANN-predicted results were very close to the experimental results with a coefficient of determination (R²) of 0.9970 and mean square error (MSE) 0.000376. Analysis based on the ANN model indicated that sorbent mass appeared to be the most influential factor in the adsorption process of Cu(II) and Pb(II). Characterization of the cobalt hydroxide nano-flakes and possible metal ions-adsorbent interactions were confirmed by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM).